In this subsection, we concern ourselves with comparing the features learned by three ResNet-50 models trained on Restricted Imagenet [1]}: without mixup, manifold mixup [2]}, and NFM. We can compare features by maximizing randomly chosen pre-logit activations of each model with respect to the input, as described by [3]}. We do so for all models with Projected Gradient Ascent over 200 iterations, a step size of 16, and an \(\ell _2\)  norm constraint of 2,000. Both the models trained with manifold mixup and NFM use an \(\alpha =0.2\) , and the NFM model uses in addition \(\sigma _{add}=2.4\)  and \(\sigma _{mult}=1.2\) . The result, as shown in Fig. REF , is that the features learned by the model trained with NFM are slightly stronger (i.e., different from random noise) than the clean model.
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